some applications are critical and must designed Fault Tolerant System. Usually Voting Algorithm is one of the principle elements of a Fault Tolerant System. Two kinds of voting algorithm are used in most applications, they are majority voting algorithm and weighted average algorithm these algorithms have some problems. Majority confronts with the problem of threshold limits and voter of weight...

some applications are critical and must designed Fault Tolerant System. Usually Voting Algorithm is one of the principle elements of a Fault Tolerant System. Two kinds of voting algorithm are used in most applications, they are majority voting algorithm and weighted average algorithm these algorithms have some problems. Majority confronts with the problem of threshold limits and voter of weight...

Support vector regression (SVR) is a learning method based on the support vector machine (SVM) that can be used for curve fitting and function estimation. In this paper, the ability of the nu-SVR to predict the catalytic activity of the Fischer-Tropsch (FT) reaction is evaluated and the result is compared with two other prediction techniques including: multilayer perceptron (MLP) and subtractiv...

Two-degree-of-freedom (2-DOF) compliant mechanism has some outstanding characteristics in accurate positioning systems. Studying the fatigue life for 2-DOF is a meaningful task to ensure long working. However, study prediction of this not been conducted so far. In article, method developed first time. This combining differential evolution algorithm and adaptive neuro-fuzzy inference system (ANF...

In this paper, an artificial neural network-based stock market prediction model was developed. Today, a lot of individuals are making predictions about the direction bond, currency, equity, and markets. Forecasting fluctuations in values is quite difficult for businesspeople industries. future value changes on markets exceedingly since there so many different economic, political, psychological ...

This paper presents a new algorithm for building an adaptive neuro-fuzzy inference system (ANFIS) from a training data set called B-ANFIS. In order to increase accuracy of the model, the following issues are executed. Firstly, a data merging rule is proposed to build and perform a data-clustering strategy. Subsequently, a combination of clustering processes in the input data space and in the jo...

In this paper, we explore the applicability of Subtractive Clustering Technique (SCT) to student allocation problem that allocates new students to homogenous groups of specified maximum capacity, and analyze effects of such allocations on the academic performance of students. The paper also presents a Fuzzy set, Subtractive Clustering Technique (SCT) and regression analysis based Subtractive Cl...

Unexpected disease outbreaks and disasters are becoming primary issues facing our world. The first points of contact either at the disaster scenes or emergency department exposed the frontline workers and medical physicians to the risk of infections. Therefore, there is a persuasive demand for the integration and exploitation of heterogeneous biomedical information to improve clinical practice,...

Inverse kinematics of robots is a critical topic in the robotics field. Although there are conventional ways solving inverse kinematics, soft computing an important technology that has lately gained prominence due to its ability reduce complexity problem. This paper presents solution using multiple adaptive neuro-fuzzy inference systems (MANFIS). Different models were established by employing v...